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Record W3130012328 · doi:10.3390/su13052471

A Conceptual Framework for Incorporation of Composting in Closed-Loop Urban Controlled Environment Agriculture

2021· article· en· W3130012328 on OpenAlex
Ajwal Dsouza, G.W. Price, Mike Dixon, Thomas Graham

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSustainability · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicComposting and Vermicomposting Techniques
Canadian institutionsDalhousie UniversityUniversity of Guelph
Fundersnot available
KeywordsReuseSustainabilityResource recoveryEnvironmental scienceAgricultureClosed loopResource (disambiguation)Production (economics)Crop residueBusinessAgricultural engineeringWaste managementEnvironmental engineeringEngineeringComputer scienceWastewaterEcology

Abstract

fetched live from OpenAlex

Controlled environment agriculture (CEA), specifically advanced greenhouses, plant factories, and vertical farms, has a significant role to play in the urban agri-food landscape through provision of fresh and nutritious food for urban populations. With the push towards improving sustainability of these systems, a circular or closed-loop approach for managing resources is desirable. These crop production systems generate biowaste in the form of crop and growing substrate residues, the disposal of which not only impacts the immediate environment, but also represents a loss of valuable resources. Closing the resource loop through composting of crop residues and urban biowaste is presented. Composting allows for the recovery of carbon dioxide and plant nutrients that can be reused as inputs for crop production, while also providing a mechanism for managing and valorizing biowastes. A conceptual framework for integrating carbon dioxide and nutrient recovery through composting in a CEA system is described along with potential environmental benefits over conventional inputs. Challenges involved in the recovery and reuse of each component, as well as possible solutions, are discussed. Supplementary technologies such as biofiltration, bioponics, ozonation, and electrochemical oxidation are presented as means to overcome some operational challenges. Gaps in research are identified and future research directions are proposed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.250
Threshold uncertainty score0.284

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.253
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it